Approach to splitting a horseshoe kidney for deceased donor transplantation
Bibliographic record
Abstract
ABSTRACT BACKGROUND The complex anatomy of horseshoe kidneys are associated with high discard rate and poor outcomes, particularly when split for transplantation. We described the surgical approach for en bloc procurement, splitting, and reconstruction of a horseshoe kidney from a deceased donor. METHODS A horseshoe kidney with multiple renal arteries and veins was procured bloc from a 27-year-old donor following neurologic brain death. Following en bloc procurement, back-table dissection facilitated vascular, collecting system reconstruction, splitting of the isthmus. The left renal moiety was transplanted into a 31-year-old female with two failed transplants, while the right moiety, with preserved isthmus, was transplanted into a 49-year-old male undergoing his first transplant. RESULTS Both grafts were transplanted successfully with immediate graft function after a cold ischemia time of 8h 40 min (left), 13h 49 min (right). Total surgery times were 3h 29min (left), 3h 37min (right). Post-operative course was unremarkable, besides one episode of acute pancreatitis managed conservatively for the recipient that received the left moiety. Nine-month follow-up showed excellent graft function in both recipients. CONCLUSION Successful transplantation of a split horseshoe kidney requires careful anatomical assessment, precise dissection, and reconstructive techniques.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".